The purpose of this paper is to identify a workable methodology to prioritise those crime scenes which have the greatest opportunity of a forensic recovery to enable effective Crime Scene Investigator (CSI) resource deployment.
The motivation behind this work stemmed from an abundance of volume crime scenes that required examination and a lack of resources that could be deployed. Within a data mining application environment, two supervised learning algorithms were used to model Northamptonshire Police's forensic data to provide a computer‐based model that could predict the outcome of finding a forensic sample at the currently unattended scene of a crime.
Based on past data, a computer model could be produced to predict the probability of finding useful fingerprints, DNA and/or footwear marks at the scene of a volume crime. In this paper, volume crime means burglary dwelling, burglary in commercial buildings, theft of and theft from motor vehicles. The model was 68 percent accurate. CSIs were 41 percent accurate in their predictions. This has been tested within five different police forces each having differing computer systems, demonstrating that the methodology is portable.
The model, when connected to either a crime recording system or an incident recording system, can produce a prioritised crime scene attendance list within minutes and assess crimes/incidents as they are reported. This list can be seamlessly used in conjunction with other attendance criteria if required, e.g. vulnerable victim, etc.
This paper provides a scientific solution to CSI resource attendance management being proved in five different UK police forces.
